VitaFALL: Advanced multi-threshold based reliable fall detection system

2020 ◽  
Vol 13 ◽  
Author(s):  
Warish D. Patel ◽  
Chirag Patel ◽  
Monal Patel
Keyword(s):  
Detection System ◽  
Wearable Sensors ◽  
Research Work ◽  
Text Message ◽  
Vital Signs ◽  
Early Death ◽  
Fall Detection ◽  
High Detection Rate ◽  
Aged People ◽  
Fall Prediction

Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for significant injuries and early death in senior citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite social and monetary impact on society. So even for the daily activity in the life of aged people, an automatically fall detecting system and vital signs examining system become a necessity. Objectives: This research work aims at helping aged people and every other necessary human by monitoring their vital signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device, could analyze the measurement in all three orthogonal directions using a triple-axis accelerometer, and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the aged and differently-abled people. Methods: Comparison with Present Algorithms, there are various benefits regarding privacy, success rate, and design of devices upgraded using an implemented algorithm over the ubiquitous algorithm. Results: As concluded from the experimental outcomes, the accuracy achieved is up to 94%, ADXL335 is a 3-Axial Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified by sending a text message via GSM and GPRS module so that aged can be helped. Conclusion: However, a delay in the time can be noticed while comparing the gradient and minimum value to predetermine the state of the older person. The experiment results show the adequacy of the proposed approach.

scholarly journals Research on the prediction model of elderly fall

2021 ◽  
Vol 336 ◽  
pp. 07019
Author(s):  
Shaohua Guo ◽  
Yinggang Xie ◽  
Yuxin Li
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Detection System ◽  
Research Work ◽  
Fall Detection ◽  
The Elderly ◽  
Wearable Device ◽  
Fall Prediction ◽  
Collection Data ◽  
Coordination Ability

As the world’s aging process accelerates, the issue of elderly safety is about to become a serious social problem. The elderly are prone to falls due to physiological reasons such as decreased physical function, weakened balance and coordination ability, and poor vision. The study of fall prediction models can predict the impending fall behavior in time before the fall, and have enough time to remind the elderly to adjust or take corresponding protective measures. Reduce the damage caused by falls to the human body, reduce the medical expenses caused by falls, and enhance the confidence of the elderly to live independently. This article gives a detailed overview of the research on the wearable device-based fall prediction system, and introduces the entire process of falling. According to the work flow of the wearable device fall detection system, it includes data collection, data preprocessing, feature extraction, and discrimination algorithms. Several aspects of the current research work are introduced, and the existing research results are classified, compared and statistically analyzed to provide meaningful reference and reference for subsequent research work. Finally, a fall prediction model based on an improved ConvLSTM is proposed.

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

scholarly journals Wireless Electroencephalography Based Blood Pressure Monitoring

2018 ◽  
Vol 7 (4.38) ◽  
pp. 904
Author(s):  
R. A. Jafri ◽  
N. Shahid ◽  
M. F. Shamim ◽  
M. A. Alam ◽  
M. W. Munir ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Monitoring ◽  
Research Work ◽  
Vital Signs ◽  
Cardiac Patients ◽  
Research Framework ◽  
Aged People ◽  
Routine Monitoring ◽  
Home Monitor ◽  
Electroencephalogram Eeg

The number of cardiac patients and aged individuals are at a rise all around the world. Taking care of such individuals is a major challenge these days. In many cases, these patients require special care and regular monitoring of vital signs like blood pressure (BP). Focusing a prevalent idea of wireless brain-computer interface (WBCI), an innovative research work is considered to meet essential routine monitoring of BP for cardiac patients and aged people without any reliance. The research framework involves the use of wireless electroencephalogram (EEG) headset to control wrist BP and arm BP monitors to determine accurate BP readings in the proposed system. An Android application "Smart Home Monitor" is developed that screens the information from the headset. The research framework is tested on ten individuals to examine the precision in BP readings from two different BP monitors. Results specify that both upper arm blood pressure readings i.e. Systolic BP readings (SBP = 119.6 ±5.1 mmHg) and Diastolic BP (DBP = 79.5 ±7.4 mmHg) were found to be better than the wrist BP readings (SBP = 128.2 ±11.7 mmHg and DBP = 83.6 ±10.3 mmHg). This examination assessed that the designed system empowers the framework to be reliable, remote and compact.  

scholarly journals Convolutional Neural Network based Intelligent Network Intrusion Detection System

2020 ◽  
Vol 6 (9) ◽  
pp. 1-4
Author(s):  
Levina Bisen ◽  
Sumit Sharma
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Research Work ◽  
Visual Fields ◽  
Intelligent Network ◽  
High Detection Rate ◽  
Network Intrusion

Today cyberspace is developing tremendously, and the Intrusion Detection System (IDS) plays a key role in information security. The IDS, which operates at the network and host levels, should be able to identify various malicious attacks. The job of network-based IDSs is to distinguish between normal and malicious traffic data and trigger an alert in the event of an attack. In addition to traditional signature-based and anomaly-based approaches, many researchers have used various deep learning (DL) techniques to detect intruders, as DL models are capable of automatically extracting salient features from the input data packets. The application of the Convolutional Neural Network (CNN), which is often used to solve research problems in the visual and visual fields, is not much explored for IDS. In this research work the proposed model for intrusion detection is based on feature selection and reduction using CNN and classification using random forest. As compared to some existing work the proposed algorithm proves its efficiency in terms of high accuracy and high detection rate.

scholarly journals Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5240
Author(s):  
Vytautas Bucinskas ◽  
Andrius Dzedzickis ◽  
Juste Rozene ◽  
Jurga Subaciute-Zemaitiene ◽  
Igoris Satkauskas ◽  
...  
Keyword(s):  
Detection System ◽  
Early Stage ◽  
Fall Detection ◽  
The Elderly ◽  
Human Gait ◽  
Normal Gait ◽  
Abnormal Gait ◽  
Fall Prediction ◽  
The Subject ◽  
Measured Pressure

Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat®-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person.

scholarly journals NT-FDS—A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2006
Author(s):  
Marvi Waheed ◽  
Hammad Afzal ◽  
Khawir Mehmood
Keyword(s):  
Deep Learning ◽  
Missing Values ◽  
Short Term Memory ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
Benchmark Datasets ◽  
Noise Tolerant ◽  
Local Interference

Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities in order to provide immediate medical assistance. Hence, the integrity of collected data becomes imperative. Presence of missing values in data, caused by unreliable data delivery, lossy sensors, local interference and synchronization disturbances and so forth, greatly hamper the credibility and usefulness of data making it unfit for reliable fall detection. This paper presents a noise tolerant FDS performing in presence of missing values in data. The work focuses on Deep Learning (DL) particularly Recurrent Neural Networks (RNNs) with an underlying Bidirectional Long Short-Term Memory (BiLSTM) stack to implement FDS based on wearable sensors. The proposed technique is evaluated on two publicly available datasets—SisFall and UP-Fall Detection. Our system produces an accuracy of 97.21% and 97.41%, sensitivity of 96.97% and 99.77% and specificity of 93.18% and 91.45% on SisFall and UP-Fall Detection respectively, thus outperforming the existing state of the art on these benchmark datasets. The resultant outcomes suggest that the ability of BiLSTM to retain long term dependencies from past and future make it an appropriate model choice to handle missing values for wearable fall detection systems.

Fall Detection System Using Wearable Sensors with Automated Notification

2021 ◽  
Author(s):  
Aini Hafizah Mohd Saod ◽  
Aisamuddin Aizat Mustafa ◽  
Zainal Hisham Che Soh ◽  
Siti Azura Ramlan ◽  
Nur Athiqah Harron
Keyword(s):  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection

scholarly journals Optimized Scale-Invariant Hog Descriptors for Tobacco Plant Detection

2021 ◽  
Vol 17 ◽  
pp. 787-794
Author(s):  
M. T. Thirthe Gowda ◽  
J. Chandrika
Keyword(s):  
Detection Rate ◽  
Performance Metrics ◽  
Detection System ◽  
Tobacco Plant ◽  
Research Work ◽  
Scale Invariant ◽  
High Detection Rate ◽  
Scale Range ◽  
Computationally Intensive ◽  
The Impact

The histogram of gradient (HOG) descriptor is being employed in this research work to demonstrate the technique of scale variant to identify the plant in surveillance videos. In few scenarios, the discrepancies in the histogram of gradient descriptors along with scale as well as variation in illumination are considered as one of the major hindrances. This research work introduces a unique SIO-HOG descriptor that is approximated to be scale-invariant. With the help of the footage that is captured from the tobacco plant identification process, the system can integrate adoptive bin selections as well as sample resizing. Further, this research work explores the impact of a PCA transform that is based on the process of feature selection on the performance of overall recognition and thereby considering finite scale range, adoptive orientation binning in non-overlapping descriptors, as well as finite scale range are all essential for a high detection rate. The feature vector of HOG over a complete search window is computationally intensive. However, suitable frameworks for classification can be developed by maintaining a precise range of attributes with finite Euclidean distance. Experimental results prove that the proposed approach for detecting tobacco from other weeds has resulted in an improved detection rate. And finally, the robustness of the complete plant detection system was evaluated on a video sequence with different non-linearity's that is quite common in a real-world environment and its performance metrics are evaluated

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